The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to embodiments of the claimed inventions.
In operations, client organizations with large datasets in their databases can benefit from predictive analysis using such data. However, client organizations are generally required to hire technical experts to develop customized mathematical constructs and predictive models, which are very expensive. Consequently, client organizations without vast financial means are priced out of the market and thus do not have access to predictive analysis capabilities for their datasets.
Client organizations that do have the financial means to hire technical and mathematical experts to develop the necessary mathematical constructs and predictive models suffer from a common problem with customized solutions. Specifically, the customized solution is tailored to the particular problem at hand at a given point in time, and as such, the customized solution is not able to accommodate changes to the underlying data structure, is not able to accommodate changes to the types of data stored within the client's datasets, and is not capable of scaling up to meet increasing and changing demands of the client as their business and dataset grows over time.